Overview

Dataset statistics

Number of variables14
Number of observations5695
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory623.0 KiB
Average record size in memory112.0 B

Variable types

Numeric14

Alerts

revenue is highly correlated with quantity_orders and 3 other fieldsHigh correlation
recency is highly correlated with quantity_orders and 2 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 7 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 3 other fieldsHigh correlation
avg_recency is highly correlated with recency and 2 other fieldsHigh correlation
frequency is highly correlated with recency and 2 other fieldsHigh correlation
frequency_btwn_purchases is highly correlated with quantity_orders and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with revenue and 3 other fieldsHigh correlation
avg_unique_basked_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
quantity_items_returned is highly correlated with quantity_orders and 1 other fieldsHigh correlation
monetary_returned is highly correlated with quantity_orders and 1 other fieldsHigh correlation
revenue is highly correlated with quantity_orders and 1 other fieldsHigh correlation
recency is highly correlated with avg_recencyHigh correlation
quantity_orders is highly correlated with revenue and 1 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 1 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_size and 2 other fieldsHigh correlation
avg_recency is highly correlated with recencyHigh correlation
avg_basket_size is highly correlated with avg_ticket and 2 other fieldsHigh correlation
quantity_items_returned is highly correlated with avg_ticket and 2 other fieldsHigh correlation
monetary_returned is highly correlated with avg_ticket and 2 other fieldsHigh correlation
revenue is highly correlated with quantity_orders and 3 other fieldsHigh correlation
recency is highly correlated with avg_recency and 1 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 2 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 2 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 1 other fieldsHigh correlation
avg_recency is highly correlated with recency and 1 other fieldsHigh correlation
frequency is highly correlated with recency and 1 other fieldsHigh correlation
frequency_btwn_purchases is highly correlated with quantity_ordersHigh correlation
avg_basket_size is highly correlated with revenue and 2 other fieldsHigh correlation
quantity_items_returned is highly correlated with monetary_returnedHigh correlation
monetary_returned is highly correlated with quantity_items_returnedHigh correlation
customer_id is highly correlated with recency and 2 other fieldsHigh correlation
revenue is highly correlated with quantity_orders and 5 other fieldsHigh correlation
recency is highly correlated with customer_id and 2 other fieldsHigh correlation
quantity_orders is highly correlated with revenue and 2 other fieldsHigh correlation
quantity_items_purchased is highly correlated with revenue and 4 other fieldsHigh correlation
avg_ticket is highly correlated with avg_basket_size and 2 other fieldsHigh correlation
avg_recency is highly correlated with customer_id and 2 other fieldsHigh correlation
time_in_base is highly correlated with customer_id and 2 other fieldsHigh correlation
frequency is highly correlated with revenue and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with revenue and 4 other fieldsHigh correlation
quantity_items_returned is highly correlated with revenue and 4 other fieldsHigh correlation
monetary_returned is highly correlated with revenue and 4 other fieldsHigh correlation
revenue is highly skewed (γ1 = 21.62884637) Skewed
quantity_items_purchased is highly skewed (γ1 = 23.05598553) Skewed
avg_ticket is highly skewed (γ1 = 27.82015631) Skewed
avg_basket_size is highly skewed (γ1 = 48.53682353) Skewed
quantity_items_returned is highly skewed (γ1 = 51.5242843) Skewed
monetary_returned is highly skewed (γ1 = 59.48544078) Skewed
customer_id has unique values Unique
quantity_items_returned has 4190 (73.6%) zeros Zeros
monetary_returned has 4190 (73.6%) zeros Zeros

Reproduction

Analysis started2022-04-22 18:36:53.388092
Analysis finished2022-04-22 18:37:53.373309
Duration59.99 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct5695
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16600.70834
Minimum12346
Maximum22709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:53.664453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12699.1
Q114288.5
median16229
Q318210.5
95-th percentile21731.1
Maximum22709
Range10363
Interquartile range (IQR)3922

Descriptive statistics

Standard deviation2808.223729
Coefficient of variation (CV)0.1691628858
Kurtosis-0.8211293405
Mean16600.70834
Median Absolute Deviation (MAD)1962
Skewness0.441165902
Sum94541034
Variance7886120.514
MonotonicityNot monotonic
2022-04-22T15:37:53.965287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
125721
 
< 0.1%
175341
 
< 0.1%
172051
 
< 0.1%
164121
 
< 0.1%
139231
 
< 0.1%
175201
 
< 0.1%
172011
 
< 0.1%
165631
 
< 0.1%
180421
 
< 0.1%
Other values (5685)5685
99.8%
ValueCountFrequency (%)
123461
< 0.1%
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
ValueCountFrequency (%)
227091
< 0.1%
227081
< 0.1%
227071
< 0.1%
227061
< 0.1%
227051
< 0.1%
227041
< 0.1%
227001
< 0.1%
226991
< 0.1%
226961
< 0.1%
226951
< 0.1%

revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5449
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1803.857041
Minimum0.42
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:54.263453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile13.171
Q1236.24
median614.66
Q31571.11
95-th percentile5323.416
Maximum279138.02
Range279137.6
Interquartile range (IQR)1334.87

Descriptive statistics

Standard deviation7897.383597
Coefficient of variation (CV)4.378054035
Kurtosis608.1754389
Mean1803.857041
Median Absolute Deviation (MAD)480.18
Skewness21.62884637
Sum10272965.85
Variance62368667.68
MonotonicityNot monotonic
2022-04-22T15:37:54.528301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
1.258
 
0.1%
4.958
 
0.1%
2.958
 
0.1%
1.657
 
0.1%
3.757
 
0.1%
12.757
 
0.1%
7.56
 
0.1%
4.256
 
0.1%
5.956
 
0.1%
Other values (5439)5623
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140450.721
< 0.1%
124564.531
< 0.1%
117379.631
< 0.1%
91062.381
< 0.1%
77183.61
< 0.1%
72882.091
< 0.1%

recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.9069359
Minimum0
Maximum373
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:54.815867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q122.5
median71
Q3200
95-th percentile338
Maximum373
Range373
Interquartile range (IQR)177.5

Descriptive statistics

Standard deviation111.6299008
Coefficient of variation (CV)0.9548612315
Kurtosis-0.643576286
Mean116.9069359
Median Absolute Deviation (MAD)61
Skewness0.8140075817
Sum665785
Variance12461.23475
MonotonicityNot monotonic
2022-04-22T15:37:55.118300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1110
 
1.9%
4105
 
1.8%
398
 
1.7%
291
 
1.6%
1086
 
1.5%
882
 
1.4%
1779
 
1.4%
979
 
1.4%
778
 
1.4%
1567
 
1.2%
Other values (294)4820
84.6%
ValueCountFrequency (%)
038
 
0.7%
1110
1.9%
291
1.6%
398
1.7%
4105
1.8%
552
0.9%
778
1.4%
882
1.4%
979
1.4%
1086
1.5%
ValueCountFrequency (%)
37323
0.4%
37222
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36615
0.3%
36519
0.3%
36411
0.2%
3627
 
0.1%

quantity_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.469710272
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:55.472359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile11
Maximum206
Range205
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.809445663
Coefficient of variation (CV)1.962540134
Kurtosis302.566861
Mean3.469710272
Median Absolute Deviation (MAD)0
Skewness13.20109159
Sum19760
Variance46.36855023
MonotonicityNot monotonic
2022-04-22T15:37:55.757310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12871
50.4%
2827
 
14.5%
3501
 
8.8%
4395
 
6.9%
5236
 
4.1%
6173
 
3.0%
7139
 
2.4%
898
 
1.7%
968
 
1.2%
1055
 
1.0%
Other values (46)332
 
5.8%
ValueCountFrequency (%)
12871
50.4%
2827
 
14.5%
3501
 
8.8%
4395
 
6.9%
5236
 
4.1%
6173
 
3.0%
7139
 
2.4%
898
 
1.7%
968
 
1.2%
1055
 
1.0%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
721
< 0.1%
622
< 0.1%
601
< 0.1%

quantity_items_purchased
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1842
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.6463565
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:56.044252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q1106
median317
Q3805.5
95-th percentile2943.3
Maximum196844
Range196843
Interquartile range (IQR)699.5

Descriptive statistics

Standard deviation4429.032218
Coefficient of variation (CV)4.525671801
Kurtosis785.3589653
Mean978.6463565
Median Absolute Deviation (MAD)253
Skewness23.05598553
Sum5573391
Variance19616326.39
MonotonicityNot monotonic
2022-04-22T15:37:56.336348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1113
 
2.0%
273
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1225
 
0.4%
8822
 
0.4%
7221
 
0.4%
720
 
0.4%
Other values (1832)5257
92.3%
ValueCountFrequency (%)
1113
2.0%
273
1.3%
351
0.9%
449
0.9%
535
 
0.6%
629
 
0.5%
720
 
0.4%
818
 
0.3%
97
 
0.1%
1017
 
0.3%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
802631
< 0.1%
773731
< 0.1%
742151
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct5454
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean582.1710525
Minimum0.42
Maximum84236.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:56.696245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.42
5-th percentile12.835
Q1158.975
median297.56
Q3486.8193956
95-th percentile1842.344
Maximum84236.25
Range84235.83
Interquartile range (IQR)327.8443956

Descriptive statistics

Standard deviation2040.79593
Coefficient of variation (CV)3.505491936
Kurtosis987.7715332
Mean582.1710525
Median Absolute Deviation (MAD)152.36
Skewness27.82015631
Sum3315464.144
Variance4164848.027
MonotonicityNot monotonic
2022-04-22T15:37:57.164381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.959
 
0.2%
1.258
 
0.1%
2.958
 
0.1%
4.958
 
0.1%
12.757
 
0.1%
3.757
 
0.1%
1.657
 
0.1%
4.256
 
0.1%
5.956
 
0.1%
7.56
 
0.1%
Other values (5444)5623
98.7%
ValueCountFrequency (%)
0.421
 
< 0.1%
0.651
 
< 0.1%
0.791
 
< 0.1%
0.844
0.1%
0.853
 
0.1%
1.071
 
< 0.1%
1.258
0.1%
1.441
 
< 0.1%
1.657
0.1%
1.691
 
< 0.1%
ValueCountFrequency (%)
84236.251
< 0.1%
77183.61
< 0.1%
52940.941
< 0.1%
50653.911
< 0.1%
21389.61
< 0.1%
18745.861
< 0.1%
14855.531
< 0.1%
14844.766671
< 0.1%
13305.51
< 0.1%
12681.581
< 0.1%

avg_recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1181
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.0251413
Minimum0
Maximum373
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:57.451870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q144.125
median86
Q3184
95-th percentile336.3
Maximum373
Range373
Interquartile range (IQR)139.875

Descriptive statistics

Standard deviation101.8129872
Coefficient of variation (CV)0.8209060368
Kurtosis-0.2554173381
Mean124.0251413
Median Absolute Deviation (MAD)55.33333333
Skewness0.9372147346
Sum706323.1796
Variance10365.88436
MonotonicityNot monotonic
2022-04-22T15:37:57.739834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6032
 
0.6%
5331
 
0.5%
21330
 
0.5%
35330
 
0.5%
18429
 
0.5%
4628
 
0.5%
6427
 
0.5%
2827
 
0.5%
7726
 
0.5%
15425
 
0.4%
Other values (1171)5410
95.0%
ValueCountFrequency (%)
04
 
0.1%
111
0.2%
27
 
0.1%
2.8473282441
 
< 0.1%
313
0.2%
3.3008849561
 
< 0.1%
3.3303571431
 
< 0.1%
3.3333333331
 
< 0.1%
418
0.3%
4.1444444441
 
< 0.1%
ValueCountFrequency (%)
37323
0.4%
37221
0.4%
37117
0.3%
3694
 
0.1%
36813
0.2%
36716
0.3%
36614
0.2%
36519
0.3%
36411
0.2%
3627
 
0.1%

time_in_base
Real number (ℝ≥0)

HIGH CORRELATION

Distinct305
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.2491659
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:58.047770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25
Q1110
median239
Q3319
95-th percentile370
Maximum374
Range373
Interquartile range (IQR)209

Descriptive statistics

Standard deviation116.5840834
Coefficient of variation (CV)0.5366376572
Kurtosis-1.233603755
Mean217.2491659
Median Absolute Deviation (MAD)96
Skewness-0.2947870121
Sum1237234
Variance13591.84851
MonotonicityNot monotonic
2022-04-22T15:37:58.329078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
374101
 
1.8%
37397
 
1.7%
36788
 
1.5%
36978
 
1.4%
36676
 
1.3%
37070
 
1.2%
35966
 
1.2%
36861
 
1.1%
37257
 
1.0%
36046
 
0.8%
Other values (295)4955
87.0%
ValueCountFrequency (%)
14
 
0.1%
211
0.2%
37
 
0.1%
413
0.2%
518
0.3%
69
0.2%
813
0.2%
96
 
0.1%
1014
0.2%
1121
0.4%
ValueCountFrequency (%)
374101
1.8%
37397
1.7%
37257
1.0%
37070
1.2%
36978
1.4%
36861
1.1%
36788
1.5%
36676
1.3%
36545
0.8%
36332
 
0.6%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1222
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02306421228
Minimum0.002673796791
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:58.642811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002673796791
5-th percentile0.00296735905
Q10.005449591281
median0.01201201201
Q30.02379800602
95-th percentile0.06879962081
Maximum1
Range0.9973262032
Interquartile range (IQR)0.01834841474

Descriptive statistics

Standard deviation0.04828311569
Coefficient of variation (CV)2.093421405
Kurtosis173.325764
Mean0.02306421228
Median Absolute Deviation (MAD)0.00750018311
Skewness10.79745302
Sum131.3506889
Variance0.00233125926
MonotonicityNot monotonic
2022-04-22T15:37:58.961782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0185185185237
 
0.6%
0.00540540540532
 
0.6%
0.0163934426231
 
0.5%
0.00282485875730
 
0.5%
0.00467289719630
 
0.5%
0.0153846153829
 
0.5%
0.0526315789529
 
0.5%
0.0192307692328
 
0.5%
0.02527
 
0.5%
0.0454545454526
 
0.5%
Other values (1212)5396
94.7%
ValueCountFrequency (%)
0.00267379679122
0.4%
0.00268096514721
0.4%
0.00268817204317
0.3%
0.0027027027033
 
0.1%
0.002710027113
0.2%
0.00271739130416
0.3%
0.0027247956414
0.2%
0.00273224043719
0.3%
0.00273972602711
0.2%
0.0027548209377
 
0.1%
ValueCountFrequency (%)
15
0.1%
0.5508021391
 
< 0.1%
0.53208556151
 
< 0.1%
0.511
0.2%
0.41
 
< 0.1%
0.33333333336
0.1%
0.33155080211
 
< 0.1%
0.31578947371
 
< 0.1%
0.27272727272
 
< 0.1%
0.26216216221
 
< 0.1%

frequency_btwn_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1225
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5475706259
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:59.264443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.01102941176
Q10.02492211838
median1
Q31
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.9750778816

Descriptive statistics

Standard deviation0.5505967909
Coefficient of variation (CV)1.005526529
Kurtosis138.7856997
Mean0.5475706259
Median Absolute Deviation (MAD)0
Skewness4.851371477
Sum3118.414715
Variance0.3031568261
MonotonicityNot monotonic
2022-04-22T15:37:59.558062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12879
50.6%
248
 
0.8%
0.062518
 
0.3%
0.0277777777817
 
0.3%
0.0238095238116
 
0.3%
0.0909090909115
 
0.3%
0.0833333333315
 
0.3%
0.0344827586214
 
0.2%
0.0294117647114
 
0.2%
0.0192307692313
 
0.2%
Other values (1215)2646
46.5%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
< 0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
< 0.1%
0.005665722381
 
< 0.1%
0.0056818181822
< 0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
41
 
< 0.1%
35
 
0.1%
248
 
0.8%
1.1428571431
 
< 0.1%
12879
50.6%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct2371
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.271079
Minimum1
Maximum74215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:37:59.853410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q175
median152
Q3290.7083333
95-th percentile734.3
Maximum74215
Range74214
Interquartile range (IQR)215.7083333

Descriptive statistics

Standard deviation1199.192546
Coefficient of variation (CV)4.470077617
Kurtosis2768.431965
Mean268.271079
Median Absolute Deviation (MAD)97
Skewness48.53682353
Sum1527803.795
Variance1438062.761
MonotonicityNot monotonic
2022-04-22T15:38:00.199212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1114
 
2.0%
272
 
1.3%
351
 
0.9%
449
 
0.9%
535
 
0.6%
629
 
0.5%
1226
 
0.5%
7222
 
0.4%
10022
 
0.4%
8821
 
0.4%
Other values (2361)5254
92.3%
ValueCountFrequency (%)
1114
2.0%
272
1.3%
351
0.9%
3.3333333331
 
< 0.1%
449
0.9%
535
 
0.6%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
629
 
0.5%
6.1428571431
 
< 0.1%
ValueCountFrequency (%)
742151
< 0.1%
40498.51
< 0.1%
141491
< 0.1%
139561
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
59631
< 0.1%
51971
< 0.1%
43001
< 0.1%
42821
< 0.1%

avg_unique_basked_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1172
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.26168455
Minimum0.2
Maximum1109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:38:00.548274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1
Q17.25
median15
Q331
95-th percentile173
Maximum1109
Range1108.8
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation76.88203211
Coefficient of variation (CV)2.063299957
Kurtosis32.88890128
Mean37.26168455
Median Absolute Deviation (MAD)10
Skewness5.073511708
Sum212205.2935
Variance5910.846861
MonotonicityNot monotonic
2022-04-22T15:38:00.836109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1277
 
4.9%
2161
 
2.8%
3114
 
2.0%
9105
 
1.8%
10105
 
1.8%
8103
 
1.8%
5102
 
1.8%
7101
 
1.8%
6101
 
1.8%
1397
 
1.7%
Other values (1162)4429
77.8%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
0.1%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.2%
0.54545454551
 
< 0.1%
0.55555555561
 
< 0.1%
0.57142857141
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
11091
< 0.1%
7481
< 0.1%
7301
< 0.1%
7201
< 0.1%
7031
< 0.1%
6861
< 0.1%
6751
< 0.1%
6731
< 0.1%
6601
< 0.1%
6491
< 0.1%

quantity_items_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct216
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.08446005
Minimum0
Maximum80995
Zeros4190
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:38:01.159291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile39
Maximum80995
Range80995
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1474.760408
Coefficient of variation (CV)31.32159541
Kurtosis2718.145124
Mean47.08446005
Median Absolute Deviation (MAD)0
Skewness51.5242843
Sum268146
Variance2174918.261
MonotonicityNot monotonic
2022-04-22T15:38:01.458605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04190
73.6%
1169
 
3.0%
2150
 
2.6%
3105
 
1.8%
489
 
1.6%
678
 
1.4%
561
 
1.1%
1252
 
0.9%
744
 
0.8%
843
 
0.8%
Other values (206)714
 
12.5%
ValueCountFrequency (%)
04190
73.6%
1169
 
3.0%
2150
 
2.6%
3105
 
1.8%
489
 
1.6%
561
 
1.1%
678
 
1.4%
744
 
0.8%
843
 
0.8%
941
 
0.7%
ValueCountFrequency (%)
809951
< 0.1%
742151
< 0.1%
93601
< 0.1%
90141
< 0.1%
80041
< 0.1%
44271
< 0.1%
37681
< 0.1%
33321
< 0.1%
28781
< 0.1%
20221
< 0.1%

monetary_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1087
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.62016681
Minimum0
Maximum168469.6
Zeros4190
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2022-04-22T15:38:01.776685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.925
95-th percentile107.4
Maximum168469.6
Range168469.6
Interquartile range (IQR)3.925

Descriptive statistics

Standard deviation2493.554888
Coefficient of variation (CV)30.18094714
Kurtosis3815.139979
Mean82.62016681
Median Absolute Deviation (MAD)0
Skewness59.48544078
Sum470521.85
Variance6217815.978
MonotonicityNot monotonic
2022-04-22T15:38:02.069291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04190
73.6%
12.7520
 
0.4%
4.9519
 
0.3%
9.9517
 
0.3%
1517
 
0.3%
5.912
 
0.2%
25.511
 
0.2%
4.2510
 
0.2%
3.759
 
0.2%
19.98
 
0.1%
Other values (1077)1382
 
24.3%
ValueCountFrequency (%)
04190
73.6%
0.422
 
< 0.1%
0.651
 
< 0.1%
0.951
 
< 0.1%
1.254
 
0.1%
1.454
 
0.1%
1.641
 
< 0.1%
1.655
 
0.1%
1.72
 
< 0.1%
1.791
 
< 0.1%
ValueCountFrequency (%)
168469.61
< 0.1%
77183.61
< 0.1%
22998.41
< 0.1%
14688.241
< 0.1%
8511.151
< 0.1%
7443.591
< 0.1%
5228.41
< 0.1%
4815.261
< 0.1%
4814.741
< 0.1%
4486.241
< 0.1%

Interactions

2022-04-22T15:37:48.627548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:02.135245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:05.305918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:08.437299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:11.562433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:14.620951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:18.260236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:21.586872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:25.628173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:29.462879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:34.633858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:37.731382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:40.653313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:43.665161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:48.903409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:02.372455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:05.504847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:08.655176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:11.753344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:14.846709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:18.519069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:21.818743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:26.049252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:29.727767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:34.929099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:37.912871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:40.828445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:43.902243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:49.205864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:02.577336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:05.703725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:08.896037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:11.957207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:15.058615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:18.718960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:22.033634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:26.357076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:30.070114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:35.131214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:38.145219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:41.068697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:44.693757image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:49.493871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:02.784218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:05.911630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:09.111915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:12.162091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:15.287525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:18.997371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:22.252559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:26.895536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:30.448503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:35.335267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:38.343109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:41.282072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:45.101633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:49.741948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:03.099038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:06.103386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:09.335793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:12.355982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:15.494073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:19.194255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:22.453751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:27.153387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:30.729346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:35.522476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:38.508751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:41.500606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:45.470081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:50.062739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:03.340899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:06.333260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:09.564282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:12.569861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:15.744946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:19.486526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:22.928559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:27.368279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:31.081142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:35.728794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:38.711412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:41.699042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:45.799158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:50.328052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:03.537789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:06.528144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:09.778141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:12.758782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:15.961813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:19.677417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:23.134467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:27.573159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:31.335995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:35.910223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:38.919015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:41.876391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:46.117021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:50.930526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:03.755667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:06.759010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:09.989023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:12.963332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:16.188323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:19.901287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:23.354115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:27.788018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:31.614448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:36.097055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:39.150149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:42.063361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:46.415958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:51.162468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:03.973555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:07.156261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:10.215518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:13.169214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:16.426163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:20.176131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:23.563995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:27.973702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:31.908281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:36.543694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:39.344531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:42.246761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:46.741881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:51.375472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:04.176970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:07.363158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:10.445390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:13.365103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:16.737985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:20.371041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:23.776881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:28.159573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:32.265078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:36.729390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:39.564324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:42.419732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:47.076978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:51.602456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:04.394813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:07.579019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:10.668274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:13.769438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:17.008404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:20.660390image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:24.041755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:28.369452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:32.695052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:36.921727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:39.817823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:42.639884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:47.395914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:51.825604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:04.612704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:07.788898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:10.883140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:13.983319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:17.378191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:20.869272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:24.487430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:28.632303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:33.322706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:37.125747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:40.025208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:42.893962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:47.722931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:52.040504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:04.854176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:07.992553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:11.111008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:14.176207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:17.672023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:21.067156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:24.820811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:28.895158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:33.656523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:37.307997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:40.209420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:43.105682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:48.018486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:52.280357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:05.082044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:08.222443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:11.338880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:14.403091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:17.958872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:21.376981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:25.170621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:29.190994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:34.310303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:37.537663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:40.435408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:43.328524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-22T15:37:48.333487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-22T15:38:02.393929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-22T15:38:02.877883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-22T15:38:03.291874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-22T15:38:03.679407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-22T15:37:52.631385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-22T15:37:53.135923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idrevenuerecencyquantity_ordersquantity_items_purchasedavg_ticketavg_recencytime_in_basefrequencyfrequency_btwn_purchasesavg_basket_sizeavg_unique_basked_sizequantity_items_returnedmonetary_returned
0178505391.21372341733158.565000186.5000003740.09090917.00000050.9705880.61764740102.58
1130473232.595691390359.17666753.2857143740.0240640.028302154.44444411.66666735143.49
2125836705.382155028447.02533324.8666673740.0401070.040323335.2000007.6000005076.04
313748948.25955439189.65000093.2500003740.0133690.01792187.8000004.80000000.00
415100876.00333380292.000000124.3333333740.0080210.07317126.6666670.33333322240.90
5152914623.3025142102330.23571426.6428573740.0374330.040115150.1428574.3571432971.79
6146885630.877213621268.13666718.6500003740.0561500.057221172.4285717.047619399523.49
7178095411.9116122057450.99250037.3000003740.0320860.033520171.4166673.8333334167.06
81531160767.9009138194667.7791214.1444443740.2433160.243316419.7142866.2307694741348.56
9160982005.63877613286.51857153.2857143740.0187170.02439087.5714294.85714300.00

Last rows

customer_idrevenuerecencyquantity_ordersquantity_items_purchasedavg_ticketavg_recencytime_in_basefrequencyfrequency_btwn_purchasesavg_basket_sizeavg_unique_basked_sizequantity_items_returnedmonetary_returned
5685226956083.951118526083.951.020.51.01852.0675.000.0
5686226967150.071121507150.071.020.51.02150.0748.000.0
5687226993686.80116913686.801.020.51.0691.0203.000.0
5688227004839.421110744839.421.020.51.01074.055.000.0
56892270417.90111417.901.020.51.014.07.000.0
5690227053.351123.351.020.51.02.02.000.0
5691227065699.001117475699.001.020.51.01747.0634.000.0
5692227076756.060120106756.060.011.01.02010.0730.000.0
5693227083217.20016543217.200.011.01.0654.056.000.0
5694227093950.72017313950.720.011.01.0731.0217.000.0